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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Blockholders (SC 13D / 13G)

get_blockholders
Read-onlyIdempotent

Returns SC 13D / SC 13G blockholder disclosures (5%+ stakes) for a US public company. Each row carries percent_owned, sole/shared voting + dispositive split, schedule_type, and the first-class going_active flag — TRUE when the same filer flipped 13G → 13D within the lookback window (the single most actionable activist signal in this dataset). Use latest_only=true (default) to dedupe to the most recent filing per filer. Use collapse_groups=true to fold multi-person filings into one row. Institutional tier only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol of the issuer.
as_of_dateNoPIT filter on accepted_at — only filings on or before this date.
latest_onlyNoWhen true (default), keep only the most recent filing per (filer, schedule prefix) — typically what analysts want. Set false to see the full filing history.
lookback_daysNoWindow for the going_active (13G → 13D) detection. Default 365 days.
lineage_detailNoPer-row provenance envelope.compact
collapse_groupsNoWhen true, fold multi-reporting-person filings into a single row, with secondary persons in the ``persons[]`` field. Default false: each person stays as its own row.
schedule_filterNoWhich schedule(s) to return. '13D' = activist (intent to influence). '13G' = passive. 'both' = no filter.both

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cikYes
rowsYes
_metaYesProvenance envelope — data lineage for every MCP response
tickerYes
company_nameYes
data_age_daysYes
staleness_warningYes

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Given that annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, the description goes beyond by detailing the fields returned (percent_owned, voting splits, going_active) and behavior of parameters like lookback_days. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences long, front-loading the core purpose, then explaining key features and usage tips. Every sentence serves a purpose with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and comprehensive annotations, the description still adds necessary context: the institutional tier restriction, the meaning of the going_active flag, and how to control deduplication and grouping. It is complete for this complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

While the input schema has 100% coverage, the description adds significant value by explaining the going_active flag, the effect of latest_only (dedup to most recent per filer), and what collapse_groups does. This clarifies the semantics beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states that the tool returns SC 13D/13G blockholder disclosures for US public companies, with a specific focus on the going_active flag. It clearly distinguishes from sibling tools like get_insider_transactions or get_institutional_holdings by specializing in 5%+ stake filings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions that the tool is 'Institutional tier only' and provides usage tips for latest_only and collapse_groups parameters. It highlights the going_active flag as the key activist signal, implying when to use this tool. However, it lacks explicit exclusions or comparisons to alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.

Resources